Status

Abstract

Vast amounts of data are collected and stored every day, as part of
corporate knowledge bases and as a response to legislative compliance
requirements.
To reduce the cost of retaining such data, compression tools are
often applied.
But simply seeking the best compression ratio is not necessarily the
most economical choice, and other factors also come in to play,
including compression and decompression throughput, the main memory
required to support a given level of on-going access to the stored
data, and the types of storage available.
Here we develop a model for the total retention cost (TRC) of a data
archiving regime, and by applying the charging rates associated with
a cloud computing provider, are able to derive dollar amounts for a
range of compression options, and hence guide the development of new
approaches that are more cost-effective than current mechanisms.
In particular, we describe an enhancement to the Relative Lempel Ziv
(RLZ) compression scheme, and show that in terms of TRC, it
outperforms previous approaches in terms of providing economical
long-term data retention.